From ﬁssion yield measurements to evaluation: status on statistical methodology for the covariance question

Chia sẻ: Huỳnh Lê Ngọc Thy | Ngày: | Loại File: PDF | Số trang:7

Thêm vào BST

Báo xấu

16
lượt xem 1
download

Download Vui lòng tải xuống để xem tài liệu đầy đủ

This study deals with the thermal neutron induced ﬁssion of 235U. The mix of data is non-unique and this topic will be discussed using the Shannon entropy criterion in the framework of the statistical methodology proposed.

Chủ đề:

Bình luận(0) Đăng nhập để gửi bình luận!

Lưu

Nội dung Text: From ﬁssion yield measurements to evaluation: status on statistical methodology for the covariance question

EPJ Nuclear Sci. Technol. 4, 26 (2018) Nuclear Sciences © B. Voirin et al., published by EDP Sciences, 2018 & Technologies https://doi.org/10.1051/epjn/2018030 Available online at: https://www.epj-n.org REGULAR ARTICLE From ﬁssion yield measurements to evaluation: status on statistical methodology for the covariance question Brieuc Voirin1,2, Grégoire Kessedjian1,*, Abdelaziz Chebboubi2, Sylvain Julien-Laferrière1,2, and Olivier Serot2 1 LPSC, Université Grenoble-Alpes, CNRS/IN2P3, 38026 Grenoble Cedex, France 2 CEA, DEN, DER, SPRC, LEPh, Cadarache Center, 13108 Saint Paul lez Durance, France Received: 5 December 2017 / Received in ﬁnal form: 21 March 2018 / Accepted: 14 May 2018 Abstract. Studies on ﬁssion yields have a major impact on the characterization and the understanding of the ﬁssion process and are mandatory for reactor applications. Fission yield evaluation represents the synthesis of experimental and theoretical knowledge to perform the best estimation of mass, isotopic and isomeric yields. Today, the output of ﬁssion yield evaluation is available as a function of isotopic yields. Without the explicitness of evaluation covariance data, mass yield uncertainties are greater than those of isotopic yields. This is in contradiction with experimental knowledge where the abundance of mass yield measurements is dominant. These last years, different covariance matrices have been suggested but the experimental part of those are neglected. The collaboration between the LPSC Grenoble and the CEA Cadarache starts a new program in the ﬁeld of the evaluation of ﬁssion products in addition to the current experimental program at Institut Laue- Langevin. The goal is to deﬁne a new methodology of evaluation based on statistical tests to deﬁne the different experimental sets in agreement, giving different solutions for different analysis choices. This study deals with the thermal neutron induced ﬁssion of 235U. The mix of data is non-unique and this topic will be discussed using the Shannon entropy criterion in the framework of the statistical methodology proposed. 1 Introduction the isotopic yields are the interesting observables for the applications, the mass yield measurements provide an Fission yields are important nuclear data for fuel cycle important constraint on the uncertainties of the isotopic studies. The mass and isotopic yields of the ﬁssion yields. The inconsistency of mass yield uncertainties comes fragments have a direct inﬂuence on the amount of from the undeﬁned covariance matrix in the current neutron poisons that limit the fuel burnup but also on the evaluations. Nevertheless, the covariance matrix depends residual power of the reactor after shutdown. Nowadays, on the evaluation process and its existence assumes that all ﬁssion yield evaluations are principally based on nuclear measurements are statistically in agreement. These last measurements dedicated to the ﬁssion process in the past years, different covariance matrices have been suggested and important information on systematic effects was not but the experimental part of those are not taken into considered. account [1–6]. Fission yield evaluation comes from data and models to Based on experimental knowledge on ﬁssion yield perform the best estimation of mass, isotopic and isomeric measurements, the goal of this study is to deﬁne a new yields. Nowaday, the mass yields are deduced from the sum methodology of evaluation based on statistical test to sort of the isotopic yields since it is the standard output of the different experimental measurements. The second evaluation ﬁles. But without any correlation matrix, their section is devoted to introduce the tools needed in the uncertainties are greater for mass yields than for isotopic discussion on the compatibility of the data. The third yields. This is in contradiction with experimental knowl- section deals with the data renormalization process and its edge where the abundance of mass yield measurements is consequence. The fourth section discusses our evaluation clearly dominant and often more accurate than isotopic procedure according to the multiplicity of solutions. yields. Thus, we expect the uncertainties on this latter Absolute normalization step of mass yields with associated observable to be lower than those on isotopic yields. Even if correlation matrix (Sect. 5) and the ranking of solutions (Sect. 6) are described in the end. And ﬁnally, conclusion and perspectives discuss the place of integral measure- * e-mail: kessedjian@lpsc.in2p3.fr ments in the evaluation framework. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2 B. Voirin et al.: EPJ Nuclear Sci. Technol. 4, 26 (2018) Table 1. P-value for ﬁve sets of data. Results are presented according to a matrix since each set can be considered as a reference dataset. No bias appears when symmetrical P-value matrix is obtained. Only two sets are in agreement for a 99.5% conﬁdence level (corresponding to a P-value greater than a quantile 1–0.995) if we consider Maeck set without the masses 135 and 136. P-values Maeck Diiorio Thierens Bail Zeynalov 8 6 Maeck 1 3 10 0.012 5 10 0 Diiorio 3 108 1 0.009 3 1024 0 Thierens 0.012 0.009 1 4 109 0 Bail 5 106 3 1024 4 109 1 0 Zeynalov 0 0 0 0 1 2 Statistical test on the compatibility of available data Fission yields are usually deﬁned with a normalization over the light and heavy fragments equal to two due to the fact that binary ﬁssion corresponds to the major ﬁssion process in comparison to the ternary ﬁssion. The structure of the mass yields, with a very low yields for the symmetric masses (≈120 amu for major actinides), allows a normali- zation to the unit only for the light or the heavy fragments. In every case, the normalization induces a constraint. Then a multinomial distribution is expected for the description of these observables. As a consequence, negative correlation is expected if there is no systematic uncertainty. Neverthe- less, the correlation matrices per measurement are not available in the database. Fig. 1. Cross-normalized data sets of ﬁssion yields for ﬁve main Through the EXFOR [7] database, we chose to test the measurements for the 235U(nth, f) reaction. methodology only on ﬁve important sets of measurements of the 235U(nth, f) reaction from Maeck et al. [8], Diiorio and Wehring [9], Thierens et al. [10], Bail [11] and Zeynalov We observe that only the data sets of Maeck and Diiorio in et al. [12]. These data correspond to more than 215 reference to Thierens one give a P-value greater than a measurements over 78 masses. With this selection, we quantile of 0.005 for a 99.5% conﬁdence level (correspond- cover at least all the heavy masses, allowing the ing to the conﬁdence level at 3 sigma for Gaussian normalization process. In this logic, at least we can assess distribution). Therefore, the validity of the normalization the absolute normalization with the heavy mass peak have to be tested for these selected data. which ﬁxes light fragment yields. This is not the usual method used by the JEFF evaluation [13] which could highlight the normalization biases. Moreover, all these data 3 Renormalization of data sets sets are presented as already normalized by the authors. Thus, assuming independent Gaussian distributions with- Many choices can be made to achieve the relative out explicit information on correlation data, we can normalization between data sets. The simplest method is calculate the x2 using the nA common measured mass to deﬁne a reference mass A0 (e.g. A0 = 136). Then, we number. This value is compared to the limited x2 value deﬁne a normalization factor to the reference set which (x2lim ) given for a 99.5% conﬁdence level. In practice, we introduces a systematic uncertainty for all the normalized calculate the P-value corresponding to the integral on data set. If we remind that measurement is the mean value [x2 ; ∞ ] range of the x2 distribution for (nA 1) degrees of of a random variable, the questioning about the normali- freedom. Table 1 presents the P-value for each bilateral zation is multiple: statistical test. The Zeynalov dataset corresponds to pre- neutron mass yields. This allows us to evaluate the – If we normalize directly via the random variables relevance of the statistical test procedure for inconsistent Y A =Y A0 , the ﬁnal distribution of mass A0, data identiﬁcation related to the others ones which are Y A0 =Y A0 ¼ 1, corresponds to a Dirac distribution post-neutron mass yields. without variance. The distribution for masses other At the ﬁrst step, we obtained a complete disagreement than the reference is the quotient of two Gaussian between all series. Therefore, we exclude the values of variables, which follows a Cauchy law. In all cases, we Maeck for the masses 135 and 136, since there is a clear create a singularity on the reference mass from the others mismatch between these values and the other ones (Fig. 1). without making physical sense.
B. Voirin et al.: EPJ Nuclear Sci. Technol. 4, 26 (2018) 3 CovðC ðAÞ; C ðA0 ÞÞ ¼ CovðN i ðAÞ; N i ðA0 ÞÞ since Cov (Nref (A) ; Nref (A0)) = 0 (no experimental covari- ance is available). Therefore, the generalized x2g can be seen as the scalar product of the vector Z on the transposed vector CT: x2g ¼ CT ⋅Z; x2g ¼ C 1 Z 1 þ . . . þ C i Z i þ . . . þ C nA Z nA with Z ¼ Cov1 ⋅C: Fig. 2. Correlation of Maeck set after renormalization to Diiorio The ith contribution to x2g (scalar), noted x2g ðiÞ set as a function of mass measured. (vector), corresponds to the ith term of the sum: x2g ðiÞ ¼ C i Z i : – The second solution proposed corresponds to a global normalization ki of all masses of ith set to the reference For the Zeynalov data set, the test gives a negative set. This solution provides simple covariance terms output due to the misclassiﬁcation. We naturally exclude between masses of a same set (Fig. 2): all these points to build the mean values of the mass yield measurements and the associated uncertainties. For the CovðN i ðAÞ; N i ðA0 ÞÞ ¼ varðki Þ: Bail data set, the global x2g value is principally given by the contribution of the mass 128 which is in disagreement with The masses used for the normalization represent the the other ones (Fig. 3). On this plot, we compare the simple common masses between the two concerned data sets and x2 calculations and the generalized x2g calculations. It is change for each normalization. The cross-covariance terms clear that the second one (x2g ) is expressly needed for a between normalized sets are almost null due to the fact that relevant test of compatibility. ki and kj ∀ i, j ∈ [1, 4] are independent if all sets are initially We also note that the relative normalization to another independent (no initial covariance). set changes according to the common masses selected. Then, In the following, we use the second method considering the selection of the data using renormalization and statistical the generalized x2 based on covariance matrices of test must have a feedback to the renormalization process to normalized sets. P -values between each set are presented limit the biases on the ﬁnal mean values of yields and their in Table 2. uncertainties. In the end, we selected the data sets of Maeck, We observe that only three sets are in agreement for a Diiorio, Thierens and Bail (except mass 128). At this step, 99.5% conﬁdence level. In Figure 1, only the Zeynalov data for instance, we can conclude for the mass 128 there are two present a clear shift to the heavy mass corresponding to a incompatible solutions: the ﬁrst one is the mean value of misclassiﬁcation in EXFOR since these data are pre- Maeck, Diiorio and Thierens and the second one is the Bail neutron yield measurements. For the Bail set, a good value. It is the same for the mass 135 and 136 from Maeck agreement is presented in Figure 1 but the statistical test which are incompatible with those of others sets. rejects this set. To go further and conclude on the reason of The x2g test allows us to make a choice on the this disagreement, we have to consider the contribution of compatibility of data with a given conﬁdence level. Thus, each mass to the statistical test. for each incompatibility, a branch of the tree of solutions is open to get all the possibilities provided by the experi- 4 Tree of solutions ments. The classical solution of the blind mean value considering, or not, penalties in case of disagreement is a In the comparison of each set Ni(A) to the reference one non-choice which washes the information given by the Na(A), due to the relative normalization (Sect. 3), we have experiments. In this method, the choice is based on a to consider the correlation matrix of Ni(A) in the regular statistical method to reach the best values with generalized x2g . limited bias and provide realistic variance–covariance matrix. x2g ¼ CT ⋅Cov1 ⋅C where C is the difference between two vectors of 5 Absolute normalization of mass yields measurements and Cov1 is the inverse covariance matrix associated to C: After the selection of compatible mass yield data, the goal is to deduce the mean values of renormalized measurements C ¼ Ni ðAÞ Nref ðAÞ and the variance–covariance matrix taking into account the covariance matrix of renormalized data (Fig. 2). The with covariance element : self-normalization of ﬁssion yields allows the determination
4 B. Voirin et al.: EPJ Nuclear Sci. Technol. 4, 26 (2018) Table 2. P-values for ﬁve sets of data after renormalization of data sets using the generalized x2 method. P -values Maeck Diiorio Thierens Bail Zeynalov 6 Maeck 1 0.959 0.010 2 10 0 Diiorio 0.959 1 0.258 4 109 0 Thierens 0.010 0.258 1 2 106 0 Bail 2 106 4 109 2 106 1 0 Zeynalov 0 0 0 0 1 Fig. 3. Contribution to the x2 and x2g values for the Bail measurements compared to the Diiorio data. (left) The mass 128 corresponds to the largest contribution to the x2 or x2g . For this plot, blue dots present simple x2 calculations and red dots correspond to generalized x2g calculations. (right) Cumulative contributions of x2 and x2g as a function of the number of masses considered. Only one mass induced a cumulative x2g value (red points) larger than the x2lim limit for 99.5% conﬁdence level (black dots). It is clear that the second calculations (x2g ) are expressly needed for a relevant mass test. of absolute yields if all the mass range is covered Choice has to be done to disentangle the four different (statistically, very low yields do not change signiﬁcantly solutions given by a single compatible dataset. the absolute normalization). Nevertheless, at this moment, an arbitrary choice is done to select the reference set needed for the renormalization. Therefore, new calculations have 6 Ranking of analysis paths been achieved changing the reference set. For the four selected data sets, the self-normalization of mean mass From our analysis, since we can change the reference data yields provides a constraint on the results. We observe a set, four solutions are obtained with very different good agreement between all mean values for the four uncertainties and correlation matrices. To interpret the evaluations as a function of mass (Fig. 4). Figure 5 presents correlation matrix, eigenvalues (EVi=1,n) are computed to the standard deviations of evaluated mass yields as a compare quantities of information provided by the function of mass for the four different reference sets. solutions [16]. The matrix traces are always equal to the Correlations of each evaluation is shown in Figure 6 and number of evaluated masses (78 in this study) but the present many important differences in the structures. The cumulative curve of eigenvalues are drastically different for uncertainty propagation method dedicated to ﬁssion yields the four solutions of the analysis (Fig. 7 (up)). We observe corresponds to the perturbation theory and is described in that only the data sets of Maeck and Diiorio in reference to references [14,15]. This is clearly due to the correlation Thierens one give a P -values greater than a quantile of matrix deduced from the renormalized data. Indeed, the 0.005 for a 99.5% conﬁdence level. These curves represent systematic uncertainties from ki=1,4 normalization factors the spectra of the correlation matrices. Two additional depend in part of the uncertainties of the reference data set. "school cases" are presented: i) a diagonal correlation
B. Voirin et al.: EPJ Nuclear Sci. Technol. 4, 26 (2018) 5 Fig. 5. Relative uncertainties of the different evaluations are Fig. 4. Evaluations of 235U(nth, f) mass yields based on reference displayed as a function of mass. Important discrepancies appear data sets. A very good agreement between evaluations and the according to the choice of the reference yield data set. JEFF3.1 library is observed. Fig. 6. For each evaluation, the correlation matrix is represented as a function of mass. Results present some large discrepancies as a function of the reference data set used for the cross-normalization of data sets. corresponding to null covariance terms; ii) an exponential where n is the number of eigenvalues. We approximate the eigenvalue spectrum. The Shannon entropy SSh is chosen as probability with the weight of each component of the a useful criterion to assess the brewing of information [17]. eigenvalue decomposition to built a relative criterion. The It is given by the relation: weight of the information is provided according to the 1 X n equation: EV i S Sh ¼ P i lnðP i Þ Pi ¼ lnð2Þ k¼1 trðCorrÞ
6 B. Voirin et al.: EPJ Nuclear Sci. Technol. 4, 26 (2018) Fig. 8. Chosen evaluation of 235U(nth, f) mass yields according to the maximum Shannon entropy in comparison to the JEFF-3.1 and the ENDF/B-VII.1 libraries. A very good agreement is observed all over the mass range for both libraries. – a maximum of entropy translates the best brewing of information. This analysis corresponds to the evaluation with a normalization based on Diiorio dataset; – a minimum of entropy corresponds to the larger constraint on the results. These two extrema could be interesting to provide the best compromise of all the data for a lower cost of uncertainties or to provide the hardest test of models according to the experimental data. Figure 8 shows the mass yield evaluation with a maximum of entropy for the Fig. 7. (Up) for each correlation matrix of evaluations, 235 U(nth, f) reaction in comparison to the JEFF-3.1.1 [13] cumulated eigenvalues (EV) are plotted as a function of the and the ENDF/B-VII.1 libraries [18]. A very good number of EV. For instance, we represent also the cumulated agreement is obtained with both libraries for this pure EV for null covariance terms and for an exponential distribu- experimental mass yield evaluation. The uncertainties of tion of EV. (Down) Shannon entropies as a function of the these results correspond to the red dots in Figure 5. data set number with respect to the index in the previous Shannon’s entropy analysis helps us to discriminate the legend. different evaluations that are similar for mean values but not for covariance. The correlation matrix (based on Diiorio renormalization) is plotted as a function of the mass range (Fig. 6b). We observe clear structures, with positive with tr(Corr) = 78 is the correlation matrix trace (in this components corresponding to the cross-normalization of study, 78 evaluated mass yields). Indeed, a large data sets and negative components from the constraint of eigenvalue reﬂects an important component of the the self-normalization of the ﬁssion yields (Sect. 5). information carried by the assessed data, a low eigenvalue corresponds to a non-signiﬁcant information due to the important correlations between mass yields. Figure 7 7 Conclusion and perspectives (down) presents the Shannon entropy calculation for a diagonal correlation matrix (null covariance term Cov = Experimental data consideration is crucial for the deﬁni- 0), an exponential spectrum of eigenvalues and the tion of the evaluation covariance in complement to spectra of the four solutions analyzed. We note that the covariance from the models. A large range of data is listed maximum of entropy appears for null covariance terms. In in the EXFOR data bank. Moreover, a lot of them covers general case, the minimal entropy is given for a full partial mass ranges which supposed a cross-normalization correlated dataset. In our example, the minimum of of data, for different incident neutron energies and not entropy corresponds to the exponential spectrum of necessarily with an absolute mass (or nuclear charge) eigenvalues. The entropie values of the mass yields identiﬁcation. The mix of all data could be non-unique and analyzed are distributed between these two extrema. this topic has been discussed in the framework of the We interpreted the results as following to select the statistical methodology proposed. This work deals with a possible choices of evaluations: general methodology to assess the ﬁssion yields and their
B. Voirin et al.: EPJ Nuclear Sci. Technol. 4, 26 (2018) 7 Unfortunately, the measurements of isotopic and isomeric distributions do not cover the range of isotopes requested for the applications. The use of models is unavoidable. Using the present results on mass yield evaluation and its covariance matrix, the goal is to validate the phenomenological ﬁssion models using a Bayesian comparison to perform a physical pre-selection of possible evaluations. Thus, for each solution, the goal is to provide a complete evaluation with its variance-covariance matrix. This second step is illustrated in Figure 6b. At the end, the benchmark on integral measurements and the cumulative yields built on the isotopic yields will allow us to reﬁne the hierarchy of the possible solutions of the parent ﬁssion yields. Author contribution statement This work corresponds to the Ph.D. work of B. Voirin and G. Kessedjian, A. Chebboubi and O. Serot are Ph.D. supervisors. All coauthors have been involved in the deﬁnition of the statistical analysis critera and the redaction of this article. This work was supported by IN2P3, the University of Grenoble Alpes, Grenoble-INP and le déﬁ NEEDS. Fig. 9. Scheme of the analysis path: statistical test is needed to identify the compatible measurements per set and not only per mass. For all series, covariance matrices are deﬁned in order to References take into account the different analysis paths. Final results represent all the solutions given by the microscopic data which 1. L. Fiorito et al., Ann. Nucl. Energy 69, 331 (2014) will be compared to integral measurements or cumulative mass 2. A. Chebboubi et al., EPJ Web Conf. 146, 04021 (2017) yields. 3. K.H. Schmidt et al., Nucl. Data Sheets 131, 107 (2016) 4. N. Terranova et al., Nucl. Data Sheets 95, 225230 (2015) 5. D. Rochman et al., Ann. Nucl. Energy 131, 125 (2016) covariance matrix. This study on the 235U(nth, f) reaction is 6. O. Leray et al., EPJ Web Conf. 146, 09023 (2017) based on statistical generalized x2g tests to build a 7. EXFOR database, https://www-nds.iaea.org/exfor/exfor. consistent data set through the existing measurements htm 8. W.J. Maeck et al., Allied Chem. Corp., Idaho Chem. present in the EXFOR database. The analysis provides Programs 1142, 09 (1978) several solutions, considering the covariance due to the 9. G. Diiorio, B.W. Wehring, Nucl. Instrum. Methods 147, 487 analysis paths. Then, a hierarchy of solutions is built (1977) according to the Shannon entropy. For this reaction, a pure 10. H. Thierens et al., Nucl. Instrum. Methods 134, 299 (1976) experimental mass yield assessment with consistent 11. A. Bail, Ph.D. thesis, Université Sciences et Technologies variance-covariance is provided due to the numerous Bordeaux I, 2009 existing data. 12. S. Zeynalov et al., in 13th International Seminar on A scheme of the procedure is shown Figure 9: same Interaction of Neutrons with Nuclei ISINN-13, Dubna, 2005 datasets provide different solutions for the mass range (e.g. 13. M.A. Kellet et al., JEFF-3.1/3.1.1, JEFF Report 20, 2009, mass 128) which are true a priori. Several solutions are ISBN 978-92-64-99087-6 funded for the covariance matrix corresponding to identical 14. F. Martin, Ph.D. thesis, Université de Grenoble, 2013 mean values of mass yields. A ranking of solutions is 15. G. Kessedjian, HDR, Université de Grenoble, 2015 proposed using the Shannon entropy to select an evalua- 16. G. Kessedjian et al., Phys. Rev. C 85, 044613 (2012) tion. Future work will proposed to compare this evaluation 17. B. Diu, C. Guthmann, B. Roulet, D. Lederer, Physique to ﬁssion models (GEF, FIFRELIN, etc.) or cumulative statistique (Hermann, 1996) data to test the consistency of these evaluated data. 18. M.B. Chadwick et al., Nucl. Data Sheets 112, 2887 (2011) Cite this article as: Brieuc Voirin, Grégoire Kessedjian, Abdelaziz Chebboubi, Sylvain Julien-Laferrière, Olivier Serot, From ﬁssion yield measurements to evaluation: status on statistical methodology for the covariance question, EPJ Nuclear Sci. Technol. 4, 26 (2018)